Abstract
Recent work has shown that a person's sympathetic arousal can be estimated from facial videos alone using basic signal processing. This opens up new possibilities in the field of telehealth and stress management, providing a non-invasive method to measure stress only using a regular RGB camera. In this paper, we present SympCam, a new 3D convolutional architecture tailored to the task of remote sympathetic arousal prediction. Our model incorporates a temporal attention module (TAM) to enhance the temporal coherence of our sequential data processing capabilities. The predictions from our method improve accuracy metrics of sympathetic arousal in prior work by 48% to a mean correlation of 0.77. We additionally compare our method with common remote photoplethysmography (rPPG) networks and show that they alone cannot accurately predict sympathetic arousal "out-of-the-box". Furthermore, we show that the sympathetic arousal predicted by our method allows detecting physical stress with a balanced accuracy of 90% - an improvement of 61% compared to the rPPG method commonly used in related work, demonstrating the limitations of using rPPG alone. Finally, we contribute a dataset designed explicitly for the task of remote sympathetic arousal prediction. Our dataset contains synchronized face and hand videos of 20 participants from two cameras synchronized with electrodermal activity (EDA) and photoplethysmography (PPG) measurements. We will make this dataset available to the community and use it to evaluate the methods in this paper. To the best of our knowledge, this is the first dataset available to other researchers designed for remote sympathetic arousal prediction.
Abstract (translated)
最近的研究表明，仅通过面部视频和基础信号处理技术就可以估算一个人的交感神经唤起程度。这为远程医疗和压力管理领域开启了新的可能性，提供了一种只需使用普通RGB摄像头即可非侵入性地测量压力的方法。在本文中，我们提出了SympCam，这是一种专为远程预测交感神经唤起任务设计的新3D卷积架构。我们的模型集成了一个时间注意力模块（TAM），以增强我们对序列数据处理的时间一致性能力。与先前研究中的方法相比，我们的方法将交感神经唤起的准确度指标提高了48%，达到平均相关系数为0.77。此外，我们将我们的方法与常见的远程光电容积脉搏波描记法（rPPG）网络进行了比较，并表明仅凭这些网络无法“开箱即用”地准确预测交感神经唤起。我们还展示了通过我们的方法预测的交感神经唤起能够以90%的平衡准确性检测到物理压力，这相较于相关研究中常用的rPPG方法有了61%的改进，证明了单独使用rPPG存在的局限性。最后，我们为远程预测交感神经唤起的任务贡献了一个专门设计的数据集。我们的数据集包含了来自两个摄像头同步录制的20名参与者的面部和手部视频，并且这些视频与皮肤电活动（EDA）和光电容积脉搏波描记法（PPG）测量同步。我们将向社区提供这一数据集并用它来评估本文中的方法。据我们所知，这是第一个专门为远程预测交感神经唤起设计并向其他研究者开放的数据集。
URL
https://arxiv.org/abs/2410.20552
